Communication devices such as wireless devices are also known as, e.g., User Equipments (UE), mobile terminals, wireless terminals and/or mobile stations. Wireless devices are enabled to communicate wirelessly in a cellular communications network or wireless communication system, sometimes also referred to as a cellular radio system, wireless communications network, or cellular network. The communication may be performed, e.g., between two wireless devices, between a wireless device and a regular telephone, and/or between a wireless device and a server via a Radio Access Network (RAN) and possibly one or more core networks, comprised within the cellular communications network.
Wireless devices may further be referred to as mobile telephones, cellular telephones, laptops, tablets or surf plates with wireless capability, just to mention some further examples. The wireless devices in the present context may be, for example, portable, pocket-storable, hand-held, computer-comprised, or vehicle-mounted mobile devices, enabled to communicate voice and/or data, via the RAN, with another entity, such as another wireless device or a server.
The wireless communications network covers a geographical area which is divided into cell areas, wherein each cell area being served by an access node such as a base station, e.g., a Radio Base Station (RBS), which sometimes may be referred to as, e.g., “Evolved Node B (eNB)”, “eNodeB”, “NodeB”, “B node”, or Base Transceiver Station (BTS), depending on the technology and terminology used. The base stations may be of different classes such as e.g. macro eNodeB, home eNodeB or pico base station, based on transmission power and thereby also cell size. A cell is the geographical area where radio coverage is provided by the base station at a base station site. One base station, situated on the base station site, may serve one or several cells. Further, each base station may support one or several communication technologies. The base stations communicate over the air interface operating on radio frequencies with the terminals within range of the base stations. In the context of this disclosure, the expression Downlink (DL) is used for the transmission path from the base station to the wireless device. The expression Uplink (UL) is used for the transmission path in the opposite direction i.e. from the wireless device to the base station.
The increasing amount of network elements in the current deployments of cellular networks is leading to an enormous complexity of operation and maintenance. Mobile experts have to deal with thousands of performance indicators, counters, alarms and configuration parameters in order to detect and diagnose problems in their networks. The concept of Self-Healing is precisely to automate those tasks of troubleshooting, such as detection, diagnosis, compensation and recovery, with the aim of reducing capital and operational expenditures and making the network more reliable.
One of the key challenges in the context of Self-Healing is the automatic search of degraded cells. The objective is to support the non-intrusive fault detection mechanisms to improve availability and reliability of the networks. The importance of this problem lies not only in developing effective reactive methods for fault detection, but also in creating proactive mechanisms that allow to anticipate and avoid the occurrence of faults. In addition, the design of effective methods to detect degradations is crucial to reduce the number of false positives of the detection algorithms, commonly called “false alarms”. Lastly, it is worth mentioning that the problem of cell degradation detection is of particular relevance in the context of heterogeneous mobile networks. For example, an outage of a Universal Mobile Telecommunications System (UMTS) cell can cause degradations on some performance indicators of an LTE cell. In this situation, it may be important to find the degradation of the LTE cell in order to aid the cell outage detection in the UMTS network.
The first mechanisms of cell degradation detection were based on monitoring metrics and establishing specific thresholds to detect if the current value of a certain metric exceeds or goes below a threshold value during a specific time. This approach is currently used in most of the existing self-healing tools and provides acceptable performance. However, the limitation of requiring human intervention to set the associated thresholds is a severe drawback. In addition, when the time evolution of metrics is analyzed, sometimes degradations are given by a peak whose values are within the normal range of the metric if the whole period is considered, i.e., the metric may be locally degraded but not globally. As a consequence, the metric may not violate the threshold and the degradation may not be detected. Furthermore, since the values of performance indicators largely depend on different factors, such as the traffic load, the type of network, etc., the thresholds may be different for different networks, complicating the procedure of threshold definition. For all these reasons, more complex approaches have been proposed in the literature. In [1], a couple of adaptive algorithms that require minimal human intervention are proposed. Unlike using fixed thresholds, the basis of these two solutions is to detect cell degradations by recognizing abnormal trends in the time evolution of the traffic data. For a certain desired level of confidence, determined by the operator, the algorithms find evidence of faults to meet such a level of confidence. Looking at their differences, the first algorithm includes a previous learning stage where a baseline profile is built by computing expected values of the metric over time. In the second alternative, instead of carrying out learning, the proposed algorithm estimates the data by looking at neighboring cells, assuming that there is an appreciable level of correlation between them. In both cases, the algorithms may adapt to variations in the network operation over time.
In [2], a method that calculates the correlation between two cells to detect degradations in cells is proposed. In that work, due to the lack of available degradations in observed real data, artificial errors in the real data were introduced. Such data are not generated by the method; they are only used for simulation purposes. The Pearson correlation coefficient between the observed cell and a neighbor cell is periodically calculated. When the metric in the observed cell starts to be degraded, due to the artificially introduced degradation, and the neighbor cell is healthy, the correlation coefficient falls below a pre-defined threshold, meaning that the degradation is detected. The choice of cells to make the comparison with the target cell is also discussed. In [3], a method for determining faults in a mobile network through pattern clustering is proposed. In particular, the fault indicators are assigned to a predetermined fault category. This category pattern is stored to form a fault category matrix. Then, all the generated matrices are clustered and the most relevant clusters are determined to identify recurrent fault category patterns and finally determine the network fault. In [4], a method for identifying the causes of changes in performance indicators by looking at the correlation with a plurality of counters is proposed. First, the candidate counters are grouped into clusters of similar counters. Then, one or more representative counters are selected from each cluster. With this method, the large problem space associated with numerous counters is effectively reduced. Finally, in [5], an integrated detection and diagnosis framework to identify anomalies and find the most probable cause of the problems is proposed. More specifically, this framework automatically generates profiles of performance indicators to characterize the faultless behavior of a network and, then, these profiles are used as reference patterns to identify significant deviations from the normal behavior.
However, the existing techniques for cell degradation detection are characterized by a poor performance, and/or are computationally complex. Therefore, with the existing techniques, operators may lose their confidence in using automated algorithms for cell degradation detection. The reason for this is that, as a consequence of the bad performance of the existing techniques, the ratio of false positives becomes high, resulting in distractions that, in some cases, require additional cost for operators.